The future of data management, integration and search could lie in semantic web technology. Baseline is arming readers with information on semantics technology by examining the niche, the opportunities and challenges it may present to business leaders, IT management and end users in the next few years.

Providing Context

The big word in semantics is “context,” says Lynda Moulton, analyst for Gilbane Group. The ultimate semantic engine, she notes, will allow you to pose a question in natural language and give you the precise answer to the questions: How does it do that? Is the technology or the search logic built into these search engines, not for just the context, but also the question that’s coming through.

“It’s got to say, first, what is she really asking about? Then, it has to say what do we have out here that’s going to match this inquiry?” Right now, there are two ways computers can search for something, Moulton explains. “One is sequentially–literally taking what you’re looking for and going through until they find a match. Then, they say ‘Here it is,’ and it keeps going and going,” she says. “Of course, that’s really slow. And, then, there’s the old technique of indexing. So, all the instances of a given word are indexed in one list with pointers going back to the document and placed in the document where that word occurred—which makes the computer find stuff a lot faster.”

As search technology has matured, it has progressed from using simple indexing techniques to more sophisticated algorithms based on linguistics, she says. For example, the verb “to rise” might appear in past tense, future tense and so on, but the index search engine will still be able to recognize it. So an index search could search the word “rose” and assume that anything related to the verb “to rise” is appropriate, she explains.

“That has obvious problems because someone who types ‘rose’ might be looking for a flower. You go through all of these unsatisfactory results because the search engine made assumptions about how you were using language and it isn’t always appropriate, so you get results that aren’t relevant,” Moulton says. “This has been the trouble of computer scientists providing search engines to find smarter and better ways of indexing things contextually.”